Type 2 diabetes has a global prevalence, with epidemiological data suggesting that some populations have a higher risk of developing this disease. However, to date, most genetic studies of type 2 ...diabetes and related glycaemic traits have been performed in individuals of European ancestry. The same is true for most other complex diseases, largely due to use of ‘convenience samples’. Rapid genotyping of large population cohorts and case–control studies from existing collections was performed when the genome-wide association study (GWAS) ‘revolution’ began, back in 2005. Although global representation has increased in the intervening 15 years, further expansion and inclusion of diverse populations in genetic and genomic studies is still needed. In this review, I discuss the progress made in incorporating multi-ancestry participants in genetic analyses of type 2 diabetes and related glycaemic traits, and associated opportunities and challenges. I also discuss how increased representation of global diversity in genetic and genomic studies is required to fulfil the promise of precision medicine for all.
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The zebrafish research community is celebrating! The zebrafish genome has recently been sequenced, the Zebrafish Mutation Project (launched by the Wellcome Trust Sanger Institute) has published the ...results of its first large-scale ethylnitrosourea (ENU) mutagenesis screen, and a host of new techniques, such as the genome editing technologies TALEN and CRISPR-Cas, are enabling specific mutations to be created in model organisms and investigated in vivo. The zebrafish truly seems to be coming of age. These powerful resources invoke the question of whether zebrafish can be increasingly used to model human disease, particularly common, chronic diseases of metabolism such as obesity and type 2 diabetes. In recent years, there has been considerable success, mainly from genomic approaches, in identifying genetic variants that are associated with these conditions in humans; however, mechanistic insights into the role of implicated disease loci are lacking. In this Review, we highlight some of the advantages and disadvantages of zebrafish to address the organism's utility as a model system for human metabolic diseases.
The discovery of human obesity-associated genes can reveal new mechanisms to target for weight loss therapy. Genetic studies of obese individuals and the analysis of rare genetic variants can ...identify novel obesity-associated genes. However, establishing a functional relationship between these candidate genes and adiposity remains a significant challenge. We uncovered a large number of rare homozygous gene variants by exome sequencing of severely obese children, including those from consanguineous families. By assessing the function of these genes in vivo in Drosophila, we identified 4 genes, not previously linked to human obesity, that regulate adiposity (itpr, dachsous, calpA, and sdk). Dachsous is a transmembrane protein upstream of the Hippo signalling pathway. We found that 3 further members of the Hippo pathway, fat, four-jointed, and hippo, also regulate adiposity and that they act in neurons, rather than in adipose tissue (fat body). Screening Hippo pathway genes in larger human cohorts revealed rare variants in TAOK2 associated with human obesity. Knockdown of Drosophila tao increased adiposity in vivo demonstrating the strength of our approach in predicting novel human obesity genes and signalling pathways and their site of action.
The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture ...of healthy thinness. Here, we characterise the heritability of thinness which we found was comparable to that of severe obesity (h2 = 28.07 vs 32.33% respectively), although with incomplete genetic overlap (r = -0.49, 95% CI -0.17, -0.82, p = 0.003). In a genome-wide association analysis of thinness (n = 1,471) vs severe obesity (n = 1,456), we identified 10 loci previously associated with obesity, and demonstrate enrichment for established BMI-associated loci (pbinomial = 3.05x10-5). Simulation analyses showed that different association results between the extremes were likely in agreement with additive effects across the BMI distribution, suggesting different effects on thinness and obesity could be due to their different degrees of extremeness. In further analyses, we detected a novel obesity and BMI-associated locus at PKHD1 (rs2784243, obese vs. thin p = 5.99x10-6, obese vs. controls p = 2.13x10-6 pBMI = 2.3x10-13), associations at loci recently discovered with much larger sample sizes (e.g. FAM150B and PRDM6-CEP120), and novel variants driving associations at previously established signals (e.g. rs205262 at the SNRPC/C6orf106 locus and rs112446794 at the PRDM6-CEP120 locus). Our ability to replicate loci found with much larger sample sizes demonstrates the value of clinical extremes and suggest that characterisation of the genetics of thinness may provide a more nuanced understanding of the genetic architecture of body weight regulation and may inform the identification of potential anti-obesity targets.
Clinical exome sequencing routinely identifies missense variants in disease-related genes, but functional characterization is rarely undertaken, leading to diagnostic uncertainty. For example, ...mutations in PPARG cause Mendelian lipodystrophy and increase risk of type 2 diabetes (T2D). Although approximately 1 in 500 people harbor missense variants in PPARG, most are of unknown consequence. To prospectively characterize PPARγ variants, we used highly parallel oligonucleotide synthesis to construct a library encoding all 9,595 possible single-amino acid substitutions. We developed a pooled functional assay in human macrophages, experimentally evaluated all protein variants, and used the experimental data to train a variant classifier by supervised machine learning. When applied to 55 new missense variants identified in population-based and clinical sequencing, the classifier annotated 6 variants as pathogenic; these were subsequently validated by single-variant assays. Saturation mutagenesis and prospective experimental characterization can support immediate diagnostic interpretation of newly discovered missense variants in disease-related genes.
The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression ...quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r(2)) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits.
Genome-wide association studies have revealed that common noncoding variants in MTNR1B (encoding melatonin receptor 1B, also known as MT(2)) increase type 2 diabetes (T2D) risk(1,2). Although the ...strongest association signal was highly significant (P < 1 × 10(-20)), its contribution to T2D risk was modest (odds ratio (OR) of ∼1.10-1.15)(1-3). We performed large-scale exon resequencing in 7,632 Europeans, including 2,186 individuals with T2D, and identified 40 nonsynonymous variants, including 36 very rare variants (minor allele frequency (MAF) <0.1%), associated with T2D (OR = 3.31, 95% confidence interval (CI) = 1.78-6.18; P = 1.64 × 10(-4)). A four-tiered functional investigation of all 40 mutants revealed that 14 were non-functional and rare (MAF < 1%), and 4 were very rare with complete loss of melatonin binding and signaling capabilities. Among the very rare variants, the partial- or total-loss-of-function variants but not the neutral ones contributed to T2D (OR = 5.67, CI = 2.17-14.82; P = 4.09 × 10(-4)). Genotyping the four complete loss-of-function variants in 11,854 additional individuals revealed their association with T2D risk (8,153 individuals with T2D and 10,100 controls; OR = 3.88, CI = 1.49-10.07; P = 5.37 × 10(-3)). This study establishes a firm functional link between MTNR1B and T2D risk.
The intensive search for genetic variants that predispose to type 2 diabetes was launched with optimism, but progress has been slower than was hoped. Even so, major advances have been made in the ...understanding of monogenic forms of the disease which together represent a substantial health burden, and a few common gene variants that influence susceptibility have now been unequivocally identified. Armed with a better understanding of the tools needed to detect such genes, it seems inevitable that the rate of progress will increase and the relevance of genetic information to the diagnosis, treatment, and prevention of diabetes will become increasingly tangible.
Hypothalamic melanocortin neurons play a pivotal role in weight regulation. Here, we examined the contribution of Semaphorin 3 (SEMA3) signaling to the development of these circuits. In genetic ...studies, we found 40 rare variants in SEMA3A-G and their receptors (PLXNA1-4; NRP1-2) in 573 severely obese individuals; variants disrupted secretion and/or signaling through multiple molecular mechanisms. Rare variants in this set of genes were significantly enriched in 982 severely obese cases compared to 4,449 controls. In a zebrafish mutagenesis screen, deletion of 7 genes in this pathway led to increased somatic growth and/or adiposity demonstrating that disruption of Semaphorin 3 signaling perturbs energy homeostasis. In mice, deletion of the Neuropilin-2 receptor in Pro-opiomelanocortin neurons disrupted their projections from the arcuate to the paraventricular nucleus, reduced energy expenditure, and caused weight gain. Cumulatively, these studies demonstrate that SEMA3-mediated signaling drives the development of hypothalamic melanocortin circuits involved in energy homeostasis.
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•Rare variants affecting Semaphorin 3 signaling are associated with human obesity•Disruption of Semaphorin 3 signaling leads to weight gain in zebrafish and mice•Semaphorin 3 signaling promotes the development of hypothalamic melanocortin circuits
Semaphorin 3 signaling promotes the development of hypothalamic circuits, and human variants are associated with obesity.
Abstract
Background
Glycemic traits—such as hyperinsulinemia, hyperglycemia, and type 2 diabetes—have been associated with higher colorectal cancer risk in observational studies; however, causality ...of these associations is uncertain. We used Mendelian randomization (MR) to estimate the causal effects of fasting insulin, 2-hour glucose, fasting glucose, glycated hemoglobin (HbA1c), and type 2 diabetes with colorectal cancer.
Methods
Genome-wide association study summary data were used to identify genetic variants associated with circulating levels of fasting insulin (n = 34), 2-hour glucose (n = 13), fasting glucose (n = 70), HbA1c (n = 221), and type 2 diabetes (n = 268). Using 2-sample MR, we examined these variants in relation to colorectal cancer risk (48 214 case patient and 64 159 control patients).
Results
In inverse-variance models, higher fasting insulin levels increased colorectal cancer risk (odds ratio OR per 1-SD = 1.65, 95% confidence interval CI = 1.15 to 2.36). We found no evidence of any effect of 2-hour glucose (OR per 1-SD = 1.02, 95% CI = 0.86 to 1.21) or fasting glucose (OR per 1-SD = 1.04, 95% CI = 0.88 to 1.23) concentrations on colorectal cancer risk. Genetic liability to type 2 diabetes (OR per 1-unit increase in log odds = 1.04, 95% CI = 1.01 to 1.07) and higher HbA1c levels (OR per 1-SD = 1.09, 95% CI = 1.00 to 1.19) increased colorectal cancer risk, although these findings may have been biased by pleiotropy. Higher HbA1c concentrations increased rectal cancer risk in men (OR per 1-SD = 1.21, 95% CI = 1.05 to 1.40), but not in women.
Conclusions
Our results support a causal effect of higher fasting insulin, but not glucose traits or type 2 diabetes, on increased colorectal cancer risk. This suggests that pharmacological or lifestyle interventions that lower circulating insulin levels may be beneficial in preventing colorectal tumorigenesis.
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